DBMS > Google BigQuery vs. Snowflake vs. Spark SQL vs. Splunk vs. TiDB

System Properties Comparison Google BigQuery vs. Snowflake vs. Spark SQL vs. Splunk vs. TiDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Analytics Platform for Big Data

TiDB is an open source distributed SQL database that supports Hybrid Transactional/Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Score	4.25
Rank	#74	Overall
	#40	Relational DBMS

Website

cloud.google.com/bigquery

Technical documentation

cloud.google.com/bigquery/docs

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

docs.splunk.com/Documentation/Splunk

docs.pingcap.com/tidb/stable

Developer

Google

Snowflake Computing Inc.

Apache Software Foundation

Splunk Inc.

PingCAP, Inc.

Initial release

2010

2014

2003

2016

Current release

3.5.0 ( 2.13), September 2023

8.1.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Limited free edition and free developer edition available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

TiDB Cloud: Fully-managed TiDB Service. Bring everything great about TiDB to the cloud.

Implementation language

Scala

Go, Rust

Server operating systems

hosted

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

Splunk Search Processing Language for search commands

yes

APIs and other access methods

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

JDBC
ODBC

HTTP REST

GORM
JDBC
ODBC
Proprietary protocol
SQLAlchemy

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

Java
Python
R
Scala

C#
Java
JavaScript
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

yes

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

yes, utilizing Spark Core

Sharding

horizontal partitioning (by key range)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with TiSpark Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

full support since version 6.6

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Since BigQuery is designed for querying data

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept, user roles and pluggable authentication

Access rights for users and roles

Fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

TiDB is an advanced open-source, distributed SQL database for modern application...
» more

Competitive advantages

- HORIZONTAL SCALING : TiDB grants total transparency into your data workloads without...
» more

Typical application scenarios

TiDB is ideal for transactional applications that require extreme scalability and...
» more

Key customers

Block, Pinterest, Catalyst, Bolt, Flipkart, Capcom, Shopee (E-commerce), JD Cloud...
» more

Market metrics

34K+ GitHub stars 5K+ members in TiDB Community Slack 1K+ community contributors...
» more

Licensing and pricing models

TiDB Community : Free open source software (Apache 2.0) TiDB Self-Hosted : Enterprise...
» more

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